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43 lines
1.6 KiB
Markdown
43 lines
1.6 KiB
Markdown
---
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title: Quantization matters
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excerpt: Open source LLMs are becoming very powerful, but pay attention to how you (or your) provider is quantizing the model. It strongly affects code editing skill.
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highlight_image: /assets/quantization.jpg
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draft: false
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nav_exclude: true
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---
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{% if page.date %}
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<p class="post-date">{{ page.date | date: "%B %d, %Y" }}</p>
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{% endif %}
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# Quantization matters
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Open source models like Qwen 2.5 32B are performing very well on
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aider's code editing benchmark, rivaling closed source frontier models.
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But pay attention to how your model is being quantized, as it
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can strongly impact code editing skill.
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Heavily quantized models are often used by cloud API providers
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and local model servers like Ollama.
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<canvas id="quantChart" width="800" height="450" style="margin: 20px 0"></canvas>
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<script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
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<script>
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{% include quant-chart.js %}
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</script>
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The graph above compares 4 different versions of the Qwen 2.5 32B model,
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served both locally and from cloud providers.
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- The [HuggingFace weights](https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct) served via [glhf.chat](https://glhf.chat).
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- The results from [OpenRouter's mix of providers](https://openrouter.ai/qwen/qwen-2.5-coder-32b-instruct/providers).
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- Two Ollama models run locally.
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The best version of the model rivals GPT-4o, while the worst performer
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is more like GPT-3.5 Turbo.
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## Choosing providers with OpenRouter
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OpenRouter allows you to ignore specific providers in your
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[preferences](https://openrouter.ai/settings/preferences).
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This can be effective to exclude highly quantized or otherwise
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undesirable providers.
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